Luca Baldassarre
Luca Baldassarre
Lead Data Scientist, Swiss Re
Verified email at - Homepage
Cited by
Cited by
Accelerated and inexact forward-backward algorithms
S Villa, S Salzo, L Baldassarre, A Verri
SIAM Journal on Optimization 23 (3), 1607-1633, 2013
Conditional mean embeddings as regressors-supplementary
S Grünewälder, G Lever, L Baldassarre, S Patterson, A Gretton, M Pontil
arXiv preprint arXiv:1205.4656, 2012
Modelling transition dynamics in MDPs with RKHS embeddings
S Grunewalder, G Lever, L Baldassarre, M Pontil, A Gretton
arXiv preprint arXiv:1206.4655, 2012
Learning-based compressive subsampling
L Baldassarre, YH Li, J Scarlett, B Gözcü, I Bogunovic, V Cevher
IEEE Journal of Selected Topics in Signal Processing 10 (4), 809-822, 2016
Structured sparsity models for brain decoding from fMRI data
L Baldassarre, J Mourao-Miranda, M Pontil
2012 Second International Workshop on Pattern Recognition in NeuroImaging, 5-8, 2012
Multi-output learning via spectral filtering
L Baldassarre, L Rosasco, A Barla, A Verri
Machine learning 87, 259-301, 2012
Convexity in source separation: Models, geometry, and algorithms
MB McCoy, V Cevher, QT Dinh, A Asaei, L Baldassarre
IEEE Signal Processing Magazine 31 (3), 87-95, 2014
Sparsity is better with stability: Combining accuracy and stability for model selection in brain decoding
L Baldassarre, M Pontil, J Mourão-Miranda
Frontiers in neuroscience 11, 62, 2017
Localizing and comparing weight maps generated from linear kernel machine learning models
J Schrouff, J Cremers, G Garraux, L Baldassarre, J Mourão-Miranda, ...
2013 International Workshop on Pattern Recognition in Neuroimaging, 124-127, 2013
Group-sparse model selection: Hardness and relaxations
L Baldassarre, N Bhan, V Cevher, A Kyrillidis, S Satpathi
IEEE Transactions on Information Theory 62 (11), 6508-6534, 2016
Vector field learning via spectral filtering
L Baldassarre, L Rosasco, A Barla, A Verri
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010
Optimal computational trade-off of inexact proximal methods
P Machart, S Anthoine, L Baldassarre
arXiv preprint arXiv:1210.5034, 2012
Structured sparsity: Discrete and convex approaches
A Kyrillidis, L Baldassarre, ME Halabi, Q Tran-Dinh, V Cevher
Compressed Sensing and its Applications: MATHEON Workshop 2013, 341-387, 2015
Model-based sketching and recovery with expanders
B Bah, L Baldassarre, V Cevher
Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete …, 2014
Adaptive learning-based compressive sampling for low-power wireless implants
C Aprile, K Ture, L Baldassarre, M Shoaran, G Yilmaz, F Maloberti, ...
IEEE Transactions on Circuits and Systems I: Regular Papers 65 (11), 3929-3941, 2018
A general framework for structured sparsity via proximal optimization
L Baldassarre, J Morales, A Argyriou, M Pontil
Artificial Intelligence and Statistics, 82-90, 2012
Structured sampling and recovery of ieeg signals
L Baldassarre, C Aprile, M Shoaran, Y Leblebici, V Cevher
2015 IEEE 6th International Workshop on Computational Advances in Multi …, 2015
On sparsity inducing regularization methods for machine learning
A Argyriou, L Baldassarre, CA Micchelli, M Pontil
Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, 205-216, 2013
Towards a theoretical framework for learning multi-modal patterns for embodied agents
N Noceti, B Caputo, C Castellini, L Baldassarre, A Barla, L Rosasco, ...
International Conference on Image Analysis and Processing, 239-248, 2009
Learning-based near-optimal area-power trade-offs in hardware design for neural signal acquisition
C Aprile, L Baldassarre, V Gupta, J Yoo, M Shoaran, Y Leblebici, ...
Proceedings of the 26th edition on Great Lakes Symposium on VLSI, 433-438, 2016
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